Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations45593
Missing cells8515
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.8 MiB
Average record size in memory1008.0 B

Variable types

Text2
Numeric7
Categorical10
DateTime2

Alerts

Delivery_location_latitude is highly overall correlated with Restaurant_latitude and 1 other fieldsHigh correlation
Delivery_location_longitude is highly overall correlated with Restaurant_longitude and 1 other fieldsHigh correlation
Delivery_person_Age is highly overall correlated with Vehicle_conditionHigh correlation
Delivery_person_Ratings is highly overall correlated with Vehicle_conditionHigh correlation
Restaurant_latitude is highly overall correlated with Delivery_location_latitude and 1 other fieldsHigh correlation
Restaurant_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Vehicle_condition is highly overall correlated with Delivery_person_Age and 2 other fieldsHigh correlation
Weatherconditions is highly overall correlated with Vehicle_conditionHigh correlation
city_code is highly overall correlated with Delivery_location_latitude and 2 other fieldsHigh correlation
Festival is highly imbalanced (86.0%) Imbalance
Delivery_person_Age has 1854 (4.1%) missing values Missing
Delivery_person_Ratings has 1908 (4.2%) missing values Missing
Time_Ordered has 1731 (3.8%) missing values Missing
Road_traffic_density has 601 (1.3%) missing values Missing
multiple_deliveries has 993 (2.2%) missing values Missing
City has 1200 (2.6%) missing values Missing
ID has unique values Unique
Restaurant_latitude has 3640 (8.0%) zeros Zeros
Restaurant_longitude has 3640 (8.0%) zeros Zeros

Reproduction

Analysis started2025-02-08 00:34:59.263111
Analysis finished2025-02-08 00:35:03.862030
Duration4.6 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct45593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2025-02-07T18:35:04.026302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9457812
Min length6

Characters and Unicode

Total characters316679
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45593 ?
Unique (%)100.0%

Sample

1st row0x4607
2nd row0xb379
3rd row0x5d6d
4th row0x7a6a
5th row0x70a2
ValueCountFrequency (%)
0x4607 1
 
< 0.1%
0x36b8 1
 
< 0.1%
0xb816 1
 
< 0.1%
0x6c6b 1
 
< 0.1%
0xd987 1
 
< 0.1%
0x5d6d 1
 
< 0.1%
0x7a6a 1
 
< 0.1%
0x70a2 1
 
< 0.1%
0x9bb4 1
 
< 0.1%
0x95b4 1
 
< 0.1%
Other values (45583) 45583
> 99.9%
2025-02-07T18:35:04.213419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%
Distinct1320
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2025-02-07T18:35:04.311023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.71035
Min length14

Characters and Unicode

Total characters670689
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDORES13DEL02
2nd rowBANGRES18DEL02
3rd rowBANGRES19DEL01
4th rowCOIMBRES13DEL02
5th rowCHENRES12DEL01
ValueCountFrequency (%)
puneres01del01 67
 
0.1%
japres11del02 67
 
0.1%
hydres04del02 66
 
0.1%
japres03del01 66
 
0.1%
vadres11del02 66
 
0.1%
ranchires02del01 66
 
0.1%
vadres08del02 66
 
0.1%
vadres11del01 65
 
0.1%
vadres14del01 65
 
0.1%
bangres03del01 65
 
0.1%
Other values (1310) 44934
98.6%
2025-02-07T18:35:04.439626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 670689
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 670689
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 670689
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 98200
14.6%
0 72942
10.9%
D 56594
 
8.4%
R 53475
 
8.0%
S 51951
 
7.7%
L 47791
 
7.1%
45593
 
6.8%
1 43745
 
6.5%
2 23301
 
3.5%
3 17335
 
2.6%
Other values (21) 159762
23.8%

Delivery_person_Age
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)0.1%
Missing1854
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean29.567137
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-02-07T18:35:04.472499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median30
Q335
95-th percentile39
Maximum50
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8151554
Coefficient of variation (CV)0.19667631
Kurtosis-1.0583326
Mean29.567137
Median Absolute Deviation (MAD)5
Skewness0.018669335
Sum1293237
Variance33.816032
MonotonicityNot monotonic
2025-02-07T18:35:04.502764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
35 2262
 
5.0%
36 2260
 
5.0%
37 2227
 
4.9%
30 2226
 
4.9%
38 2219
 
4.9%
24 2210
 
4.8%
32 2202
 
4.8%
22 2196
 
4.8%
29 2191
 
4.8%
33 2187
 
4.8%
Other values (12) 21559
47.3%
ValueCountFrequency (%)
15 38
 
0.1%
20 2136
4.7%
21 2153
4.7%
22 2196
4.8%
23 2087
4.6%
24 2210
4.8%
25 2174
4.8%
26 2159
4.7%
27 2150
4.7%
28 2179
4.8%
ValueCountFrequency (%)
50 53
 
0.1%
39 2144
4.7%
38 2219
4.9%
37 2227
4.9%
36 2260
5.0%
35 2262
5.0%
34 2166
4.8%
33 2187
4.8%
32 2202
4.8%
31 2120
4.6%

Delivery_person_Ratings
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)0.1%
Missing1908
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.6337805
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-02-07T18:35:04.533326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.33471641
Coefficient of variation (CV)0.07223398
Kurtosis15.670705
Mean4.6337805
Median Absolute Deviation (MAD)0.2
Skewness-2.4935516
Sum202426.7
Variance0.11203507
MonotonicityNot monotonic
2025-02-07T18:35:04.567466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.8 7148
15.7%
4.7 7142
15.7%
4.9 7041
15.4%
4.6 6940
15.2%
5 3996
8.8%
4.5 3303
7.2%
4.1 1430
 
3.1%
4.2 1418
 
3.1%
4.3 1409
 
3.1%
4.4 1361
 
3.0%
Other values (18) 2497
 
5.5%
(Missing) 1908
 
4.2%
ValueCountFrequency (%)
1 38
0.1%
2.5 20
< 0.1%
2.6 22
< 0.1%
2.7 22
< 0.1%
2.8 19
< 0.1%
2.9 19
< 0.1%
3 6
 
< 0.1%
3.1 29
0.1%
3.2 29
0.1%
3.3 25
0.1%
ValueCountFrequency (%)
6 53
 
0.1%
5 3996
8.8%
4.9 7041
15.4%
4.8 7148
15.7%
4.7 7142
15.7%
4.6 6940
15.2%
4.5 3303
7.2%
4.4 1361
 
3.0%
4.3 1409
 
3.1%
4.2 1418
 
3.1%

Restaurant_latitude
Real number (ℝ)

High correlation  Zeros 

Distinct657
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.017729
Minimum-30.905562
Maximum30.914057
Zeros3640
Zeros (%)8.0%
Negative431
Negative (%)0.9%
Memory size356.3 KiB
2025-02-07T18:35:04.602778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30.905562
5-th percentile0
Q112.933284
median18.546947
Q322.728163
95-th percentile26.913987
Maximum30.914057
Range61.819619
Interquartile range (IQR)9.794879

Descriptive statistics

Standard deviation8.185109
Coefficient of variation (CV)0.48097541
Kurtosis3.713716
Mean17.017729
Median Absolute Deviation (MAD)5.482766
Skewness-1.3615831
Sum775889.3
Variance66.996009
MonotonicityNot monotonic
2025-02-07T18:35:04.641036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
26.911378 182
 
0.4%
26.914142 180
 
0.4%
26.892312 176
 
0.4%
26.90294 176
 
0.4%
26.902908 176
 
0.4%
26.88842 174
 
0.4%
26.905287 173
 
0.4%
26.913726 173
 
0.4%
22.308096 172
 
0.4%
Other values (647) 40371
88.5%
ValueCountFrequency (%)
-30.905562 1
 
< 0.1%
-30.902872 2
< 0.1%
-30.899584 3
< 0.1%
-30.895817 3
< 0.1%
-30.893384 1
 
< 0.1%
-30.893244 1
 
< 0.1%
-30.892978 1
 
< 0.1%
-30.890184 1
 
< 0.1%
-30.885915 1
 
< 0.1%
-30.885814 1
 
< 0.1%
ValueCountFrequency (%)
30.914057 42
0.1%
30.905562 37
0.1%
30.902872 32
0.1%
30.899992 38
0.1%
30.899584 41
0.1%
30.895817 36
0.1%
30.895204 41
0.1%
30.893384 38
0.1%
30.893244 38
0.1%
30.893234 39
0.1%

Restaurant_longitude
Real number (ℝ)

High correlation  Zeros 

Distinct518
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.231332
Minimum-88.366217
Maximum88.433452
Zeros3640
Zeros (%)8.0%
Negative162
Negative (%)0.4%
Memory size356.3 KiB
2025-02-07T18:35:04.678129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-88.366217
5-th percentile0
Q173.17
median75.898497
Q378.044095
95-th percentile85.325347
Maximum88.433452
Range176.79967
Interquartile range (IQR)4.874095

Descriptive statistics

Standard deviation22.883647
Coefficient of variation (CV)0.32583245
Kurtosis10.303039
Mean70.231332
Median Absolute Deviation (MAD)2.161724
Skewness-3.2201594
Sum3202057.1
Variance523.66131
MonotonicityNot monotonic
2025-02-07T18:35:04.807171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
75.789034 182
 
0.4%
75.805704 181
 
0.4%
75.793007 177
 
0.4%
75.806896 176
 
0.4%
75.792934 176
 
0.4%
75.75282 174
 
0.4%
75.800689 174
 
0.4%
75.794592 173
 
0.4%
73.167753 173
 
0.4%
Other values (508) 40367
88.5%
ValueCountFrequency (%)
-88.366217 1
 
< 0.1%
-88.352885 1
 
< 0.1%
-88.349843 1
 
< 0.1%
-88.322337 1
 
< 0.1%
-85.33982 1
 
< 0.1%
-85.335486 1
 
< 0.1%
-85.325731 3
< 0.1%
-85.325447 2
< 0.1%
-85.325146 1
 
< 0.1%
-85.3172 1
 
< 0.1%
ValueCountFrequency (%)
88.433452 35
0.1%
88.433187 36
0.1%
88.400581 34
0.1%
88.400467 33
0.1%
88.39331 36
0.1%
88.393294 38
0.1%
88.368628 35
0.1%
88.36783 33
0.1%
88.366217 33
0.1%
88.365507 37
0.1%

Delivery_location_latitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.465186
Minimum0.01
Maximum31.054057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-02-07T18:35:04.846819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q112.988453
median18.633934
Q322.785049
95-th percentile27.023726
Maximum31.054057
Range31.044057
Interquartile range (IQR)9.796596

Descriptive statistics

Standard deviation7.335122
Coefficient of variation (CV)0.41998534
Kurtosis0.26434584
Mean17.465186
Median Absolute Deviation (MAD)5.47924
Skewness-0.70106646
Sum796290.22
Variance53.804015
MonotonicityNot monotonic
2025-02-07T18:35:04.922610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
31.054057 3
< 0.1%
31.045562 4
< 0.1%
31.044057 4
< 0.1%
31.042872 2
< 0.1%
31.039992 3
< 0.1%
31.039584 4
< 0.1%
31.035817 4
< 0.1%
31.035562 3
< 0.1%
31.035204 4
< 0.1%
31.033384 4
< 0.1%

Delivery_location_longitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.845702
Minimum0.01
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-02-07T18:35:04.962794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q173.28
median76.002574
Q378.107044
95-th percentile85.375486
Maximum88.563452
Range88.553452
Interquartile range (IQR)4.827044

Descriptive statistics

Standard deviation21.118812
Coefficient of variation (CV)0.29809588
Kurtosis7.1044509
Mean70.845702
Median Absolute Deviation (MAD)2.196673
Skewness-2.9563849
Sum3230068.1
Variance446.00422
MonotonicityNot monotonic
2025-02-07T18:35:05.002302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
88.563452 2
< 0.1%
88.563187 4
< 0.1%
88.543452 3
< 0.1%
88.543187 4
< 0.1%
88.530581 4
< 0.1%
88.530467 3
< 0.1%
88.523452 4
< 0.1%
88.52331 4
< 0.1%
88.523294 2
< 0.1%
88.523187 2
< 0.1%

Order_Date
Categorical

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
15-03-2022
 
1192
03-04-2022
 
1178
13-03-2022
 
1169
26-03-2022
 
1166
24-03-2022
 
1162
Other values (39)
39726 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters455930
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19-03-2022
2nd row25-03-2022
3rd row19-03-2022
4th row05-04-2022
5th row26-03-2022

Common Values

ValueCountFrequency (%)
15-03-2022 1192
 
2.6%
03-04-2022 1178
 
2.6%
13-03-2022 1169
 
2.6%
26-03-2022 1166
 
2.6%
24-03-2022 1162
 
2.5%
09-03-2022 1159
 
2.5%
05-04-2022 1157
 
2.5%
05-03-2022 1154
 
2.5%
07-03-2022 1153
 
2.5%
03-03-2022 1150
 
2.5%
Other values (34) 33953
74.5%

Length

2025-02-07T18:35:05.035398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15-03-2022 1192
 
2.6%
03-04-2022 1178
 
2.6%
13-03-2022 1169
 
2.6%
26-03-2022 1166
 
2.6%
24-03-2022 1162
 
2.5%
09-03-2022 1159
 
2.5%
05-04-2022 1157
 
2.5%
05-03-2022 1154
 
2.5%
07-03-2022 1153
 
2.5%
03-03-2022 1150
 
2.5%
Other values (34) 33953
74.5%

Most occurring characters

ValueCountFrequency (%)
2 157344
34.5%
0 110378
24.2%
- 91186
20.0%
3 39515
 
8.7%
1 24441
 
5.4%
4 11271
 
2.5%
5 5423
 
1.2%
6 4969
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 455930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 157344
34.5%
0 110378
24.2%
- 91186
20.0%
3 39515
 
8.7%
1 24441
 
5.4%
4 11271
 
2.5%
5 5423
 
1.2%
6 4969
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 455930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 157344
34.5%
0 110378
24.2%
- 91186
20.0%
3 39515
 
8.7%
1 24441
 
5.4%
4 11271
 
2.5%
5 5423
 
1.2%
6 4969
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 455930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 157344
34.5%
0 110378
24.2%
- 91186
20.0%
3 39515
 
8.7%
1 24441
 
5.4%
4 11271
 
2.5%
5 5423
 
1.2%
6 4969
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Time_Ordered
Date

Missing 

Distinct176
Distinct (%)0.4%
Missing1731
Missing (%)3.8%
Memory size356.3 KiB
Minimum2025-02-07 00:00:00
Maximum2025-02-07 23:55:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-07T18:35:05.069011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:05.108682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct193
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Minimum2025-02-07 00:00:00
Maximum2025-02-07 23:55:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-07T18:35:05.146493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:05.186361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Weatherconditions
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
conditions Fog
7654 
conditions Stormy
7586 
conditions Cloudy
7536 
conditions Sandstorms
7495 
conditions Windy
7422 
Other values (2)
7900 

Length

Max length21
Median length17
Mean length16.790845
Min length14

Characters and Unicode

Total characters765545
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconditions Sunny
2nd rowconditions Stormy
3rd rowconditions Sandstorms
4th rowconditions Sunny
5th rowconditions Cloudy

Common Values

ValueCountFrequency (%)
conditions Fog 7654
16.8%
conditions Stormy 7586
16.6%
conditions Cloudy 7536
16.5%
conditions Sandstorms 7495
16.4%
conditions Windy 7422
16.3%
conditions Sunny 7284
16.0%
conditions NaN 616
 
1.4%

Length

2025-02-07T18:35:05.222802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.256349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
conditions 45593
50.0%
fog 7654
 
8.4%
stormy 7586
 
8.3%
cloudy 7536
 
8.3%
sandstorms 7495
 
8.2%
windy 7422
 
8.1%
sunny 7284
 
8.0%
nan 616
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 765545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 765545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 765545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 121457
15.9%
n 120671
15.8%
i 98608
12.9%
d 68046
8.9%
t 60674
7.9%
s 60583
7.9%
c 45593
 
6.0%
45593
 
6.0%
y 29828
 
3.9%
S 22365
 
2.9%
Other values (10) 92127
12.0%

Road_traffic_density
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing601
Missing (%)1.3%
Memory size2.7 MiB
Low
15477 
Jam
14143 
Medium
10947 
High
4425 

Length

Max length7
Median length4
Mean length4.8282806
Min length4

Characters and Unicode

Total characters217234
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowJam
3rd rowLow
4th rowMedium
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 15477
33.9%
Jam 14143
31.0%
Medium 10947
24.0%
High 4425
 
9.7%
(Missing) 601
 
1.3%

Length

2025-02-07T18:35:05.299982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.323342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 15477
34.4%
jam 14143
31.4%
medium 10947
24.3%
high 4425
 
9.8%

Most occurring characters

ValueCountFrequency (%)
44992
20.7%
m 25090
11.5%
L 15477
 
7.1%
o 15477
 
7.1%
w 15477
 
7.1%
i 15372
 
7.1%
J 14143
 
6.5%
a 14143
 
6.5%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (5) 35169
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 217234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
44992
20.7%
m 25090
11.5%
L 15477
 
7.1%
o 15477
 
7.1%
w 15477
 
7.1%
i 15372
 
7.1%
J 14143
 
6.5%
a 14143
 
6.5%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (5) 35169
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 217234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
44992
20.7%
m 25090
11.5%
L 15477
 
7.1%
o 15477
 
7.1%
w 15477
 
7.1%
i 15372
 
7.1%
J 14143
 
6.5%
a 14143
 
6.5%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (5) 35169
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 217234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
44992
20.7%
m 25090
11.5%
L 15477
 
7.1%
o 15477
 
7.1%
w 15477
 
7.1%
i 15372
 
7.1%
J 14143
 
6.5%
a 14143
 
6.5%
M 10947
 
5.0%
e 10947
 
5.0%
Other values (5) 35169
16.2%

Vehicle_condition
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2
15034 
1
15030 
0
15009 
3
 
520

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45593
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Length

2025-02-07T18:35:05.351591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.372162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Snack
11533 
Meal
11458 
Drinks
11322 
Buffet
11280 

Length

Max length7
Median length6
Mean length6.2444235
Min length5

Characters and Unicode

Total characters284702
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnack
2nd rowSnack
3rd rowDrinks
4th rowBuffet
5th rowSnack

Common Values

ValueCountFrequency (%)
Snack 11533
25.3%
Meal 11458
25.1%
Drinks 11322
24.8%
Buffet 11280
24.7%

Length

2025-02-07T18:35:05.401672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.425058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
snack 11533
25.3%
meal 11458
25.1%
drinks 11322
24.8%
buffet 11280
24.7%

Most occurring characters

ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45593
16.0%
a 22991
 
8.1%
n 22855
 
8.0%
k 22855
 
8.0%
e 22738
 
8.0%
f 22560
 
7.9%
S 11533
 
4.1%
c 11533
 
4.1%
l 11458
 
4.0%
M 11458
 
4.0%
Other values (7) 79128
27.8%

Type_of_vehicle
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
motorcycle
26435 
scooter
15276 
electric_scooter
3814 
bicycle
 
68

Length

Max length17
Median length11
Mean length10.49229
Min length8

Characters and Unicode

Total characters478375
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowscooter
3rd rowmotorcycle
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Length

2025-02-07T18:35:05.454445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.475998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 478375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 478375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 478375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 91050
19.0%
c 79724
16.7%
e 53221
11.1%
t 49339
10.3%
r 49339
10.3%
45593
9.5%
l 30317
 
6.3%
y 26503
 
5.5%
m 26435
 
5.5%
s 19090
 
4.0%
Other values (3) 7764
 
1.6%

multiple_deliveries
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing993
Missing (%)2.2%
Memory size2.5 MiB
1
28159 
0
14095 
2
 
1985
3
 
361

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44600
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 28159
61.8%
0 14095
30.9%
2 1985
 
4.4%
3 361
 
0.8%
(Missing) 993
 
2.2%

Length

2025-02-07T18:35:05.505259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.525694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28159
63.1%
0 14095
31.6%
2 1985
 
4.5%
3 361
 
0.8%

Festival
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing228
Missing (%)0.5%
Memory size2.6 MiB
No
44469 
Yes
 
896

Length

Max length4
Median length3
Mean length3.0197509
Min length3

Characters and Unicode

Total characters136991
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 44469
97.5%
Yes 896
 
2.0%
(Missing) 228
 
0.5%

Length

2025-02-07T18:35:05.552484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.570633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 44469
98.0%
yes 896
 
2.0%

Most occurring characters

ValueCountFrequency (%)
45365
33.1%
N 44469
32.5%
o 44469
32.5%
Y 896
 
0.7%
e 896
 
0.7%
s 896
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45365
33.1%
N 44469
32.5%
o 44469
32.5%
Y 896
 
0.7%
e 896
 
0.7%
s 896
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45365
33.1%
N 44469
32.5%
o 44469
32.5%
Y 896
 
0.7%
e 896
 
0.7%
s 896
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45365
33.1%
N 44469
32.5%
o 44469
32.5%
Y 896
 
0.7%
e 896
 
0.7%
s 896
 
0.7%

City
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing1200
Missing (%)2.6%
Memory size3.0 MiB
Metropolitian
34093 
Urban
10136 
Semi-Urban
 
164

Length

Max length14
Median length14
Mean length12.162323
Min length6

Characters and Unicode

Total characters539922
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowMetropolitian
3rd rowUrban
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian 34093
74.8%
Urban 10136
 
22.2%
Semi-Urban 164
 
0.4%
(Missing) 1200
 
2.6%

Length

2025-02-07T18:35:05.597680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T18:35:05.620431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 34093
76.8%
urban 10136
 
22.8%
semi-urban 164
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 68350
12.7%
t 68186
12.6%
o 68186
12.6%
r 44393
8.2%
a 44393
8.2%
n 44393
8.2%
44393
8.2%
e 34257
6.3%
M 34093
6.3%
p 34093
6.3%
Other values (6) 55185
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 539922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 68350
12.7%
t 68186
12.6%
o 68186
12.6%
r 44393
8.2%
a 44393
8.2%
n 44393
8.2%
44393
8.2%
e 34257
6.3%
M 34093
6.3%
p 34093
6.3%
Other values (6) 55185
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 539922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 68350
12.7%
t 68186
12.6%
o 68186
12.6%
r 44393
8.2%
a 44393
8.2%
n 44393
8.2%
44393
8.2%
e 34257
6.3%
M 34093
6.3%
p 34093
6.3%
Other values (6) 55185
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 539922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 68350
12.7%
t 68186
12.6%
o 68186
12.6%
r 44393
8.2%
a 44393
8.2%
n 44393
8.2%
44393
8.2%
e 34257
6.3%
M 34093
6.3%
p 34093
6.3%
Other values (6) 55185
10.2%

Time_taken(min)
Real number (ℝ)

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.294607
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-02-07T18:35:05.650043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q332
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3838061
Coefficient of variation (CV)0.3568719
Kurtosis-0.31079787
Mean26.294607
Median Absolute Deviation (MAD)7
Skewness0.48595123
Sum1198850
Variance88.055818
MonotonicityNot monotonic
2025-02-07T18:35:05.686820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 2123
 
4.7%
25 2050
 
4.5%
27 1976
 
4.3%
28 1965
 
4.3%
29 1956
 
4.3%
19 1824
 
4.0%
15 1810
 
4.0%
18 1765
 
3.9%
16 1706
 
3.7%
17 1696
 
3.7%
Other values (35) 26722
58.6%
ValueCountFrequency (%)
10 750
1.6%
11 757
1.7%
12 746
1.6%
13 716
 
1.6%
14 739
1.6%
15 1810
4.0%
16 1706
3.7%
17 1696
3.7%
18 1765
3.9%
19 1824
4.0%
ValueCountFrequency (%)
54 91
 
0.2%
53 100
 
0.2%
52 79
 
0.2%
51 94
 
0.2%
50 72
 
0.2%
49 280
0.6%
48 277
0.6%
47 295
0.6%
46 274
0.6%
45 241
0.5%

city_code
Categorical

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
JAP
3443 
RANCHI
3229 
BANG
3195 
SUR
3187 
HYD
3181 
Other values (17)
29358 

Length

Max length6
Median length3
Mean length3.6606058
Min length3

Characters and Unicode

Total characters166898
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDO
2nd rowBANG
3rd rowBANG
4th rowCOIMB
5th rowCHEN

Common Values

ValueCountFrequency (%)
JAP 3443
 
7.6%
RANCHI 3229
 
7.1%
BANG 3195
 
7.0%
SUR 3187
 
7.0%
HYD 3181
 
7.0%
MUM 3173
 
7.0%
MYS 3171
 
7.0%
COIMB 3170
 
7.0%
VAD 3166
 
6.9%
INDO 3159
 
6.9%
Other values (12) 13519
29.7%

Length

2025-02-07T18:35:05.721552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jap 3443
 
7.6%
ranchi 3229
 
7.1%
bang 3195
 
7.0%
sur 3187
 
7.0%
hyd 3181
 
7.0%
mum 3173
 
7.0%
mys 3171
 
7.0%
coimb 3170
 
7.0%
vad 3166
 
6.9%
indo 3159
 
6.9%
Other values (12) 13519
29.7%

Most occurring characters

ValueCountFrequency (%)
N 16600
 
9.9%
A 15948
 
9.6%
M 12687
 
7.6%
H 12481
 
7.5%
D 11001
 
6.6%
U 10953
 
6.6%
C 10245
 
6.1%
I 9558
 
5.7%
O 8439
 
5.1%
P 8006
 
4.8%
Other values (10) 50980
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16600
 
9.9%
A 15948
 
9.6%
M 12687
 
7.6%
H 12481
 
7.5%
D 11001
 
6.6%
U 10953
 
6.6%
C 10245
 
6.1%
I 9558
 
5.7%
O 8439
 
5.1%
P 8006
 
4.8%
Other values (10) 50980
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16600
 
9.9%
A 15948
 
9.6%
M 12687
 
7.6%
H 12481
 
7.5%
D 11001
 
6.6%
U 10953
 
6.6%
C 10245
 
6.1%
I 9558
 
5.7%
O 8439
 
5.1%
P 8006
 
4.8%
Other values (10) 50980
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16600
 
9.9%
A 15948
 
9.6%
M 12687
 
7.6%
H 12481
 
7.5%
D 11001
 
6.6%
U 10953
 
6.6%
C 10245
 
6.1%
I 9558
 
5.7%
O 8439
 
5.1%
P 8006
 
4.8%
Other values (10) 50980
30.5%

Interactions

2025-02-07T18:35:03.154106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.298294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.631335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.029243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.291856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.552793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.813237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.204823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.355619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.685938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.073988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.335943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.596591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.855636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.253342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.407420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.737190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.116203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.376891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.638007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.968891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.292294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.452511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.783958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.150561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.411782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.672798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.002782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.334167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.497145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.830121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.185411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.445959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.707607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.036376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.378022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.542115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.876482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.220860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.480954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.741600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.073501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.419770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.584603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:01.921837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.254151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.514566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:02.775582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T18:35:03.111397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-07T18:35:05.753235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CityDelivery_location_latitudeDelivery_location_longitudeDelivery_person_AgeDelivery_person_RatingsFestivalOrder_DateRestaurant_latitudeRestaurant_longitudeRoad_traffic_densityTime_taken(min)Type_of_orderType_of_vehicleVehicle_conditionWeatherconditionscity_codemultiple_deliveries
City1.0000.0010.0010.0620.0610.1050.0530.0000.0040.0750.2780.0070.0360.0630.0380.0000.130
Delivery_location_latitude0.0011.0000.1220.004-0.0100.0000.1810.9730.1160.0180.0300.0000.0110.0000.0000.8880.009
Delivery_location_longitude0.0010.1221.0000.008-0.0080.0000.1380.1110.9880.0010.0280.0000.0090.0000.0000.7740.004
Delivery_person_Age0.0620.0040.0081.000-0.0960.0690.0000.0030.0060.0000.3110.0070.2430.5770.4080.0000.079
Delivery_person_Ratings0.061-0.010-0.008-0.0961.0000.0810.035-0.007-0.0040.100-0.2940.0050.2520.5880.4180.0000.111
Festival0.1050.0000.0000.0690.0811.0000.1090.0020.0080.1260.4250.0000.0560.1000.0700.0000.208
Order_Date0.0530.1810.1380.0000.0350.1091.0000.1390.0990.2130.1240.0000.0120.0080.0080.2170.098
Restaurant_latitude0.0000.9730.1110.003-0.0070.0020.1391.0000.1220.0060.0150.0000.0650.1640.1180.6170.011
Restaurant_longitude0.0040.1160.9880.006-0.0040.0080.0990.1221.0000.0020.0090.0000.1090.2510.2550.3780.000
Road_traffic_density0.0750.0180.0010.0000.1000.1260.2130.0060.0021.0000.2650.0000.0000.0060.0000.0000.109
Time_taken(min)0.2780.0300.0280.311-0.2940.4250.1240.0150.0090.2651.0000.0000.1050.1820.1260.0080.337
Type_of_order0.0070.0000.0000.0070.0050.0000.0000.0000.0000.0000.0001.0000.0000.0030.0000.0000.007
Type_of_vehicle0.0360.0110.0090.2430.2520.0560.0120.0650.1090.0000.1050.0001.0000.4570.2000.0000.047
Vehicle_condition0.0630.0000.0000.5770.5880.1000.0080.1640.2510.0060.1820.0030.4571.0000.5300.0000.075
Weatherconditions0.0380.0000.0000.4080.4180.0700.0080.1180.2550.0000.1260.0000.2000.5301.0000.0000.068
city_code0.0000.8880.7740.0000.0000.0000.2170.6170.3780.0000.0080.0000.0000.0000.0001.0000.009
multiple_deliveries0.1300.0090.0040.0790.1110.2080.0980.0110.0000.1090.3370.0070.0470.0750.0680.0091.000

Missing values

2025-02-07T18:35:03.507750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T18:35:03.641411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-07T18:35:03.786293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderedTime_Order_pickedWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken(min)city_code
00x4607INDORES13DEL02374.922.74504975.89247122.76504975.91247119-03-202211:30:0011:45:00conditions SunnyHigh2Snackmotorcycle0NoUrban24.0INDO
10xb379BANGRES18DEL02344.512.91304177.68323713.04304177.81323725-03-202219:45:0019:50:00conditions StormyJam2Snackscooter1NoMetropolitian33.0BANG
20x5d6dBANGRES19DEL01234.412.91426477.67840012.92426477.68840019-03-202208:30:0008:45:00conditions SandstormsLow0Drinksmotorcycle1NoUrban26.0BANG
30x7a6aCOIMBRES13DEL02384.711.00366976.97649411.05366977.02649405-04-202218:00:0018:10:00conditions SunnyMedium0Buffetmotorcycle1NoMetropolitian21.0COIMB
40x70a2CHENRES12DEL01324.612.97279380.24998213.01279380.28998226-03-202213:30:0013:45:00conditions CloudyHigh1Snackscooter1NoMetropolitian30.0CHEN
50x9bb4HYDRES09DEL03224.817.43166878.40832117.46166878.43832111-03-202221:20:0021:30:00conditions CloudyJam0Buffetmotorcycle1NoUrban26.0HYD
60x95b4RANCHIRES15DEL01334.723.36974685.33982023.47974685.44982004-03-202219:15:0019:30:00conditions FogJam1Mealscooter1NoMetropolitian40.0RANCHI
70x9eb2MYSRES15DEL02354.612.35205876.60665012.48205876.73665014-03-202217:25:0017:30:00conditions CloudyMedium2Mealmotorcycle1NoMetropolitian32.0MYS
80x1102HYDRES05DEL02224.817.43380978.38674417.56380978.51674420-03-202220:55:0021:05:00conditions StormyJam0Buffetmotorcycle1NoMetropolitian34.0HYD
90xcdcdDEHRES17DEL01364.230.32796878.04610630.39796878.11610612-02-202221:55:0022:10:00conditions FogJam2Snackmotorcycle3NoMetropolitian46.0DEH
IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderedTime_Order_pickedWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken(min)city_code
455830x5193MYSRES13DEL02364.812.31097276.65926412.44097276.78926418-03-202221:10:0021:20:00conditions SunnyJam2Drinkselectric_scooter1NoUrban29.0MYS
455840xa333CHENRES08DEL02374.813.02239480.24243913.04239480.26243905-04-202209:35:0009:50:00conditions SandstormsLow2Drinkselectric_scooter0NoMetropolitian20.0CHEN
455850xc9abKNPRES03DEL01304.226.46900380.31634426.53900380.38634414-02-202218:10:0018:25:00conditions CloudyMedium1Snackmotorcycle2YesMetropolitian42.0KNP
455860x4e21BANGRES16DEL03284.913.02919877.57099713.05919877.60099730-03-202221:55:0022:00:00conditions SandstormsJam1Mealscooter1NoMetropolitian29.0BANG
455870x1178RANCHIRES16DEL01354.223.37129285.32787223.48129285.43787208-03-202221:45:0021:55:00conditions WindyJam2Drinksmotorcycle1NoMetropolitian33.0RANCHI
455880x7c09JAPRES04DEL01304.826.90232875.79425726.91232875.80425724-03-202211:35:0011:45:00conditions WindyHigh1Mealmotorcycle0NoMetropolitian32.0JAP
455890xd641AGRRES16DEL01214.60.0000000.0000000.0700000.07000016-02-202219:55:0020:10:00conditions WindyJam0Buffetmotorcycle1NoMetropolitian36.0AGR
455900x4f8dCHENRES08DEL03304.913.02239480.24243913.05239480.27243911-03-202223:50:0000:05:00conditions CloudyLow1Drinksscooter0NoMetropolitian16.0CHEN
455910x5eeeCOIMBRES11DEL01204.711.00175376.98624111.04175377.02624107-03-202213:35:0013:40:00conditions CloudyHigh0Snackmotorcycle1NoMetropolitian26.0COIMB
455920x5fb2RANCHIRES09DEL02234.923.35105885.32573123.43105885.40573102-03-202217:10:0017:15:00conditions FogMedium2Snackscooter1NoMetropolitian36.0RANCHI